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高维随机矩阵描述下的量测大数据建模与异常数据检测方法

DOI: 10.13334/j.0258-8013.pcsee.2015.S.008, PP. 59-66

Keywords: 量测大数据,高维随机矩阵,时空同步建模,异常数据检测

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Abstract:

随着互联电网运行方式的愈加复杂多变以及广域量测系统部署的越来越完善,以广域测量系统(wideareameasurementsystem,WAMS)量测大数据为基础的实时稳定分析成为必然要求。与此同时,如何对全网多节点毫秒级海量WAMS大数据进行时空同步处理和异常数据检测,成为阻碍其发挥更大作用的关键问题。因此,该文提出基于高维随机矩阵描述的WAMS量测大数据建模与分析方法。首先在对WAMS量测数据时空特性分析的基础上,根据高维随机矩阵理论,进行了WAMS量测大数据的高维随机矩阵模型构建,然后推导了其异常数据检测理论和方法,最后在仿真算例上模拟实测量测数据,通过对比不同异常时刻量测数据的Trace检测和谱分布,验证了该量测大数据的建模方法的有效性与适用性。

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